TWEAK and cIAP1 Regulate Myoblast Fusion Through the Noncanonical NF-κB Signaling Pathway
Bibliographic record
Abstract
The fusion of mononucleated muscle progenitor cells (myoblasts) into multinucleated muscle fibers is a critical aspect of muscle development and regeneration. We identified the noncanonical nuclear factor κB (NF-κB) pathway as a signaling axis that drives the recruitment of myoblasts into new muscle fibers. Loss of cellular inhibitor of apoptosis 1 (cIAP1) protein led to constitutive activation of the noncanonical NF-κB pathway and an increase in the number of nuclei per myotube. Knockdown of essential mediators of NF-κB signaling, such as p100, RelB, inhibitor of κB kinase α, and NF-κB-inducing kinase, attenuated myoblast fusion in wild-type myoblasts. In contrast, the extent of myoblast fusion was increased when the activity of the noncanonical NF-κB pathway was enhanced by increasing the abundance of p52 and RelB or decreasing the abundance of tumor necrosis factor (TNF) receptor-associated factor 3, an inhibitor of this pathway. Low concentrations of the cytokine TNF-like weak inducer of apoptosis (TWEAK), which preferentially activates the noncanonical NF-κB pathway, also increased myoblast fusion, without causing atrophy or impairing myogenesis. These results identify roles for TWEAK, cIAP1, and noncanonical NF-κB signaling in the regulation of myoblast fusion and highlight a role for cytokine signaling during adult skeletal myogenesis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".